Google Professional Machine Learning Engineer Question 290
Select 3Google Cloud PlatformYou are tasked with training a deep learning model on a large dataset using Vertex AI. The model requires distributed training to minimize training time. You have access to both TPUs and GPUs, and you decide to implement Horovod for distributed training. Which of the following steps are required to ensure efficient distributed training in this scenario?
- A
Set up a reduction server to coordinate gradient aggregation across multiple workers.
- B
Ensure that each worker has access to the same dataset and preprocesses it independently.
- C
Configure TPU-specific libraries to optimize Horovod's communication layer.
- D
Use Horovod's AllReduce algorithm for gradient synchronization across devices.
- E
Manually implement gradient synchronization logic in the training script.
Show answer and explanation
Correct answers: A, B, D
Explanation
Efficient distributed training with Horovod on Vertex AI requires setting up a reduction server for gradient aggregation, ensuring consistent and independent data preprocessing across workers, and leveraging Horovod's built-in AllReduce algorithm for gradient synchronization. These steps optimize training performance while simplifying implementation. Manually implementing synchronization or focusing on TPU-specific optimizations for Horovod's communication layer is unnecessary.
- A. Correct.
Correct. A reduction server is necessary in distributed training to aggregate gradients from multiple workers, especially when using Horovod on Vertex AI.
- B. Correct.
Correct. Distributed training requires consistent data distribution, and ensuring that each worker preprocesses the dataset independently maintains scalability.
- C. Incorrect.
Incorrect. While TPUs have specific libraries like TensorFlow's TPU API, optimizing Horovod's communication layer does not require TPU-specific libraries.
- D. Correct.
Correct. Horovod's AllReduce algorithm is a key feature used for gradient synchronization across devices to ensure model consistency.
- E. Incorrect.
Incorrect. Horovod abstracts the gradient synchronization process, so manual implementation is unnecessary and inefficient.